Modeling and optimization the effective parameters of nanofluid heat transfer performance using artificial neural network and genetic algorithm method-Reference-Cited by-同舟云学术

Modeling and optimization the effective parameters of nanofluid heat transfer performance using artificial neural network and genetic algorithm method

Published:2021-11 Issue: Volume:7 Page:8447-8464
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Pourpasha Hadi,Farshad Pedram,Zeinali Heris Saeed^ORCID

Publisher

Elsevier BV

Subject

General Energy

Reference45 articles.

1. An experimental study on the rheological behavior of hybrid tungsten oxide (WO3)-MWCNTs/engine oil Newtonian nanofluids;Aghahadi;J. Mol. Struct.,2019

2. Prediction of the pressure drop for CuO/(Ethylene glycol-water) nanofluid flows in the car radiator by means of Artificial Neural Networks analysis integrated with genetic algorithm;Ahmadi;Physica A,2020

3. Modeling and prediction of the specific heat capacity of Al2 O3/water nanofluids using hybrid genetic algorithm/support vector regression model;Alade;Nano-Struct. Nano-Objects,2019

4. Experimental investigation on the thermal performance of compact heat exchanger and the rheological properties of low concentration mono and hybrid nanofluids containing Al2O3 and CuO nanoparticles;Asokan;Therm. Sci. Eng. Prog.,2020

5. Entropy generation analysis of different nanofluid flows in the space between two concentric horizontal pipes in the presence of magnetic field: single-phase and two-phase approaches;Barnoon;Comput. Math. Appl.,2019

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